snapshot of the dashboard product created by the author

Besides predictive models, Dashboard is another great way to unleash the power of data by displaying and visualizing data for domain experts to analyze. Building on top of the idea of the dashboard, periodically updating it with new data can reflect a real-time data visualization, hence produce more accurate analysis.

For my last project at Metis, our task is — Using a large to massive scale dataset obtained by any means, engineer an end-to-end data storage and processing pipeline that provides a useful service in any domain of interest.

Since all my previous projects have linked to COVID-19 one way…


Image detection with deep neural learning is something I’ve been looking forward to learn since I started my journey in data science. My background is in medical diagnostic sonography and echocardiography. And I was amazed by the application of deep learning techniques for detections of diseases/tumors using X-ray images. Although I didn’t choose to do medical images for the current project, I look forward to the applications in the future when there’s sufficient amount of medical image database, specifically ultrasound and echocardiogram images.

For the 6th project at Metis, we’re tasked to use primarily non-tabular data (images, text, time series…


Photo by Jaredd Craig on Unsplash

For the 5th module’s project at Metis Data Science Bootcamp, we’re tasked to build unsupervised learning models that address a useful structure finding, topic modeling, and/or recommendation system in any domain of interest using data with primarily textual information.

For my project, I took the approach of Latent Dirichlet Allocation(LDA) for topic modeling on a large collection of textual data. And I was amazed at how powerful it can be.

Before jumping into the project, I’d like to share some helpful articles here instead of reinventing the wheel:

Natural Language Processing(NLP)


Image source: https://www.newfoodmagazine.com/article/145042/food-and-drink-technology-predictions/

“Predicting the future isn’t magic, it’s artificial intelligence.” — Dave Waters

Everyone loves the idea of being clairvoyant, yet not everyone knows we have this hidden ability by using Machine Learning ;)

We’ve learned supervised learning in regression a few weeks ago. And in weeks 7 and 8, we’re learning the other category of supervised learning — Classification. And it is a big one! There are not just a lot more algorithms to learn but also the metrics that evaluate the model performances (e.g. Confusion matrix is still confusing). Here’s a great article to read if you’re just getting started…


Photo by Carlos Muza on Unsplash

One of the reasons Data Scientist is coined the “Sexiest Job of the 21st century” is because it can effectively use collected data to create transformative products and services that could be the most viable and effective way of solving extreme challenges for businesses/organizations.

In weeks 5 and 6 at Metis Bootcamp, we dived into the “business” side of data science — how to identify, design, and scope data science projects. And what would be the best way to practice these concepts? You got it — Projects!

For the business project, we have to deliver a well-scoped project proposal and…


Photo by National Cancer Institute on Unsplash

Domain — Healthcare

Company — Oncora Medical

Article — https://appliedradiationoncology.com/articles/varian-oncora-pair-up-to-accelerate-precision-medicine

Opportunity: to help physicians make informed treatment and patient care decisions

Impacts:

  1. Minimize risk of toxicity
  2. Maximize chance for cure
  3. Improve care for patients

Of the problem types introduced in the lecture, which one does this use case most closely align?

Prescriptive — recommend an optimal cancer treatment plan for each patient

Prediction — predicting the outcome of cancer treatment plans

How did the company/organization use data science to achieve the desired impact?

Using historical treatment data to build predictive models that help design an optimal treatment plan for cancer…


Photo by Yuyeung Lau on Unsplash

Week 3 and 4 at Metis Bootcamp was definitely increasingly onerous. This module combined two major topics — webscraping and linear regression. If you’re like me, with no math or stats background, some of the theories and concepts may seem a bit abstract by explaining with equations. Luckily, I found this channel on Youtube — StatQuest, that explains the concepts in graphs and made it so much easier to understand. Hope this is helpful to you as well!

Same as the last module, there’s a project due by the end of the second week. The second project at Metis is…


photo credit — Marian Carrasquero for The New York Times

As a Metis Data Science Bootcamp student, our first project is to perform an exploratory data analysis on NYC MTA turnstile data. We have to come up with a potential client who might find this data analysis useful and with a backstory for the analysis.

On top of all the things we’re learning in the first week, it seems like an impossible task. But it ties in with the curriculum pretty well and gives us the chance to put all the skills we just learned the past two weeks into practice.

Disclaimer: I am new to machine learning and also…

Crystal Huang

Data Scientist @ Metis, with a dash of passion in medical image analysis. https://www.linkedin.com/in/crystal-huang-ds/

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